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Commit d9c206e

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Algorithmica
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‎2019-october/8.geometrical viz - classification algorithms/non linear models-1.py‎

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import xgboost as xgb
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#2-d classification pattern
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X, y = cutils.generate_linear_synthetic_data_classification(n_samples=1000, n_features=2, n_classes=2, weights=[0.5, 0.5], class_sep=2)
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X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1)
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cutils.plot_data_2d_classification(X, y)
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‎2019-october/8.geometrical viz - classification algorithms/non linear models-2.py‎

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import classification_utils as cutils
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from sklearn import model_selection, linear_model, svm, preprocessing, pipeline, neural_network
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8-
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#2-d classification pattern
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X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1)
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X, y = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=1000, noise=0.1)
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cutils.plot_data_2d_classification(X_train, y_train)
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#perceptron algorithm
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stages = [('features', kutils.KernelTransformer('rbf')) ,
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('clf', linear_model.Perceptron(max_iter=1000))
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]
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stages = [
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('features', preprocessing.PolynomialFeatures()),
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('clf', linear_model.Perceptron(max_iter=1000))
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]
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perceptron_pipeline = pipeline.Pipeline(stages)
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perceptron_pipeline_grid = {'features__gamma':[0.1, 0.01, 0.2]}
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pipeline_object = cutils.grid_search_best_model(perceptron_pipeline, perceptron_pipeline_grid, X_train, y_train)
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cutils.plot_model_2d_classification(pipeline_object, X_train, y_train)
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#logistic regression algorithm
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stages = [('features', kutils.KernelTransformer('rbf')) ,
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('clf', linear_model.LogisticRegression())
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]
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stages = [
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('features', preprocessing.PolynomialFeatures()),
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('clf', linear_model.LogisticRegression())
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]
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lr_pipeline = pipeline.Pipeline(stages)
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lr_pipeline_grid = {'features__gamma':[0.1, 1, 5,10]}
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cutils.plot_model_2d_classification(pipeline_object, X_train, y_train)
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#linear svm algorithm
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kernel_svm_estimator = svm.SVC(kernel='rbf')
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kernel_svm_grid = {'gamma':[0.01, 0.1, 1, 2, 5, 10] }
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final_estimator = cutils.grid_search_best_model(kernel_svm_estimator, kernel_svm_grid, X_train, y_train)
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stages = [
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('features', preprocessing.PolynomialFeatures()),
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('clf', svm.LinearSVC())
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]
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svm_pipeline = pipeline.Pipeline(stages)
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svm_pipeline_grid = {'clf__penalty':['l2'], 'clf__C':[0.01, 0.1, 0.3, 0.5], 'features__degree':[2,3,5,10, 50, 100]}
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pipeline_object = cutils.grid_search_best_model(svm_pipeline, svm_pipeline_grid, X_train, y_train)
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final_estimator = pipeline_object.named_steps['clf']
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print(final_estimator.intercept_)
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cutils.plot_model_2d_classification(final_estimator, X_train, y_train)
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#artifical neural network
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ann_estimator = neural_network.MLPClassifier()
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ann_grid = {'hidden_layer_sizes':[(3, 4), (10, 20)] }
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final_estimator = cutils.grid_search_best_model(ann_estimator, ann_grid, X_train, y_train)
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print(final_estimator.intercepts_)
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print(final_estimator.coefs_)
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cutils.plot_model_2d_classification(final_estimator, X_train, y_train)
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print(final_estimator.coef_)
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cutils.plot_model_2d_classification(pipeline_object, X_train, y_train)
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import sys
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sys.path.append("E:/New Folder/utils")
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import kernel_utils as kutils
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import classification_utils as cutils
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from sklearn import model_selection, linear_model, svm, preprocessing, pipeline, neural_network
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#2-d classification pattern
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X, y = cutils.generate_nonlinear_synthetic_data_classification2(n_samples=1000, noise=0.1)
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X, y = cutils.generate_nonlinear_synthetic_data_classification3(n_samples=1000, noise=0.1)
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cutils.plot_data_2d_classification(X, y)
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X_train, X_test, y_train, y_test = model_selection.train_test_split(X, y, test_size=0.2, random_state=1)
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cutils.plot_data_2d_classification(X_train, y_train)
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#perceptron algorithm
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stages = [('features', kutils.KernelTransformer('rbf')) ,
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('clf', linear_model.Perceptron(max_iter=1000))
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]
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perceptron_pipeline = pipeline.Pipeline(stages)
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perceptron_pipeline_grid = {'features__gamma':[0.1, 0.01, 0.2]}
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pipeline_object = cutils.grid_search_best_model(perceptron_pipeline, perceptron_pipeline_grid, X_train, y_train)
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final_estimator = pipeline_object.named_steps['clf']
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print(final_estimator.intercept_)
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print(final_estimator.coef_)
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cutils.plot_model_2d_classification(pipeline_object, X_train, y_train)
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#logistic regression algorithm
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stages = [('features', kutils.KernelTransformer('rbf')) ,
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('clf', linear_model.LogisticRegression())
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]
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lr_pipeline = pipeline.Pipeline(stages)
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lr_pipeline_grid = {'features__gamma':[0.1, 1, 5,10]}
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pipeline_object = cutils.grid_search_best_model(lr_pipeline, lr_pipeline_grid, X_train, y_train)
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final_estimator = pipeline_object.named_steps['clf']
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print(final_estimator.intercept_)
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cutils.plot_model_2d_classification(pipeline_object, X_train, y_train)
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#linear svm algorithm
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stages = [('features', kutils.KernelTransformer('poly')) ,
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('clf', svm.LinearSVC())
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]
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kernel_svm_pipeline = pipeline.Pipeline(stages)
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kernel_svm_pipeline_grid = {'features__degree':[2,3,4]}
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pipeline_object = cutils.grid_search_best_model(kernel_svm_pipeline, kernel_svm_pipeline_grid, X_train, y_train)
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final_estimator = pipeline_object.named_steps['clf']
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print(final_estimator.intercept_)
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print(final_estimator.coef_)
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cutils.plot_model_2d_classification(pipeline_object, X_train, y_train)
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#out of box kernel based svm
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kernel_svm_estimator = svm.SVC(kernel='rbf')
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kernel_svm_grid = {'gamma':[0.01, 0.1, 1, 2, 5, 10], 'C':[0.001, 0.01, 0.1, 0.5] }
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final_estimator = cutils.grid_search_best_model(kernel_svm_estimator, kernel_svm_grid, X_train, y_train)
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print(final_estimator.intercept_)
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cutils.plot_model_2d_classification(final_estimator, X_train, y_train)
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kernel_svm_estimator = svm.SVC(kernel='poly')
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kernel_svm_grid = {'degree':[2, 3, 4], 'C':[0.001, 0.01, 0.1, 0.5] }
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final_estimator = cutils.grid_search_best_model(kernel_svm_estimator, kernel_svm_grid, X_train, y_train)
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print(final_estimator.intercept_)
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cutils.plot_model_2d_classification(final_estimator, X_train, y_train)
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#artifical neural network
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ann_estimator = neural_network.MLPClassifier()
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ann_grid = {'hidden_layer_sizes':[(3, 4), (10, 20)] }
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final_estimator = cutils.grid_search_best_model(ann_estimator, ann_grid, X_train, y_train)
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print(final_estimator.intercepts_)
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print(final_estimator.coefs_)
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cutils.plot_model_2d_classification(final_estimator, X_train, y_train)

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